Suqing Duan, Jiangyu Wu, Shuai Chen, Yizhun Peng
College of Electronic Information and Automation, Tianjin University of Science and Technology, China
pp. 214–223
ABSTRACT
The rapid economic development has fueled the demand for enhancing lifestyle and home aesthetics, leading to the growing popularity of leisure activities and home decoration. As a response, the ornamental fish industry has flourished, prompting fish enthusiasts to seek efficient ways to care for their fish. Smart fish box has emerged as popular solutions, offering features such as remote control and monitoring. Smart fish box incorporates machine vision and Internet of Things technologies, allowing users to remotely control lighting, water changing, feeding, and oxygen pump operations. Temperature sensors transmit data to a mobile app, enabling users to monitor and adjust water temperature. These boxes also features built-in cameras for real-time monitoring and send notifications when fish food is running low. This innovative design addresses several challenges in ornamental fish care. This paper presents the mechanical structure, control circuitry, and vision algorithm of the smart fish box. By utilizing collected data, a neural network is trained on the Raspberry Pi platform, successfully recognizing fish health status.

ARTICLE INFO
Article History
Received 20 November 2022
Accepted 28 June 2023

Keywords
Machine vision
Internet of things
Remote control
Neural network
Machine learning

JAALR3405

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